Genetic signatures in the envelope glycoproteins of HIV-1 that associate with broadly neutralizing antibodies

A steady increase in knowledge of the molecular and antigenic structure of the gp120 and gp41 HIV-1 envelope glycoproteins (Env) is yielding important new insights for vaccine design, but it has been difficult to translate this information to an immunogen that elicits broadly neutralizing antibodies. To help bridge this gap, we used phylogenetically corrected statistical methods to identify amino acid signature patterns in Envs derived from people who have made potently neutralizing antibodies, with the hypothesis that these Envs may share common features that would be useful for incorporation in a vaccine immunogen. Before attempting this, essentially as a control, we explored the utility of our computational methods for defining signatures of complex neutralization phenotypes by analyzing Env sequences from 251 clonal viruses that were differentially sensitive to neutralization by the well-characterized gp120-specific monoclonal antibody, b12. We identified ten b12-neutralization signatures, including seven either in the b12-binding surface of gp120 or in the V2 region of gp120 that have been previously shown to impact b12 sensitivity. A simple algorithm based on the b12 signature pattern was predictive of b12 sensitivity/resistance in an additional blinded panel of 57 viruses. Upon obtaining these reassuring outcomes, we went on to apply these same computational methods to define signature patterns in Env from HIV-1 infected individuals who had potent, broadly neutralizing responses. We analyzed a checkerboard-style neutralization dataset with sera from 69 HIV-1-infected individuals tested against a panel of 25 different Envs. Distinct clusters of sera with high and low neutralization potencies were identified. Six signature positions in Env sequences obtained from the 69 samples were found to be strongly associated with either the high or low potency responses. Five sites were in the CD4-induced coreceptor binding site of gp120, suggesting an important role for this region in the elicitation of broadly neutralizing antibody responses against HIV-1.     

The function of TLR4 in interferon gamma or interleukin-13 exposed and lipopolysaccharide stimulated gingival epithelial cell cultures

Gingival epithelial cells are part of the first line of host defense against infection. Toll-like receptors (TLRs) serve important immune and nonimmune functions. We investigated how interferon gamma (INF-γ) and interleukin 13 (IL-13) are involved in the TLR4 ligand-induced regulation of interleukin-8 (IL-8) effects on gingival epithelial cells. We used immunohistochemistry to localize TLR4 in ten healthy and ten periodontitis tissue specimens. Gingival epithelial cells then were primed with Th1 cytokine (INF-γ) or Th2 cytokine (IL-13) before stimulation with Escherichia coli-derived lipopolysaccharide (LPS) and enzyme-linked immunosorbent assay (ELISA) was performed to detect the level of IL-8 secretion in cell culture supernatants. Although both healthy and periodontitis gingival tissue samples expressed TLR4, the periodontitis samples showed more intense expression on gingival epithelial cells. Gingival epithelial cell cultures were primed with either INF-γ or IL-13 before stimulation with TLR4 ligand. Supernatants from co-stimulated epithelial cells exhibited IL-8 production in opposite directions, i.e., as one stimulates the release, the other reduces the release. INF-γ significantly increased TLR4 function, whereas IL-13 significantly decreased TLR4 function, i.e., production of IL-8. Pathogen associated molecular pattern-LPS, shared by many different periodonto-pathogenic bacteria, activates the gingival epithelial cells in a TLR-dependent manner. Diminished or increased TLR function in gingival epithelial cells under the influence of different Th cell types may protect or be harmful due to the altered TLR signaling.     

Geographic variations of seasonality and coexistence in communities: The role of diversity and climate

One of the most conspicuous and widely analyzed patterns in ecology is the latitudinal gradient in species richness. Over the 200 years since its recognition, several hypotheses have accumulated in order to account for spatial variations in diversity. Geographic variations in seasonality have been repeatedly proposed as a determinant of community richness. However, the geographic structure of community seasonality has not yet been analyzed. In the present work we evaluated three hypotheses that account for variations in the temporal structuring of communities: first, environmental seasonality determines community seasonality; second, community richness determines its degree of structuring; and third, the presence of an increase in species segregation with latitude, reflected in a pattern of species negative co‐occurrence. The hypotheses were evaluated using path analysis on 29 amphibian communities from South America, connecting latitude, environmental conditions, diversity, seasonality, and coexistence structure – nestedness and negative co‐occurrence – within communities. Latitude positively affects community seasonality through an increase in temperature seasonality, but a weak negative direct effect suggests that other variables not considered in the model – such as the strength of biotic interactions – could also be involved. Both latitude and diversity (directly and indirectly) determine an increase in negative co‐occurrence and nestedness. This suggests that groups of species that are mutually nested in time are internally segregated. Further, the strength of this structure is determined by community diversity and latitude. Temporal structuring of a community is associated with latitude and diversity, pointing to the existence of a systematic change in community organization far beyond, but probably interrelated, with the recognized latitudinal trend in richness. The available information and analysis supported the three hypotheses evaluated.     

Effects of contrasting demographic histories on selection at major histocompatibility complex loci in two sympatric species of tuco‐tucos (Rodentia: Ctenomyidae)

To explore the impact of history on selection and genetic structure at functional loci, we compared patterns of major histocompatibility complex (MHC) variability in two sympatric species of ctenomyid rodents with different demographic backgrounds. Although Ctenomys talarum has experienced a stable demographic history, Ctenomys australis has undergone a recent demographic expansion. Accordingly, we predicted that MHC allele frequency distributions should be more skewed, differences between coding and noncoding regions should be less pronounced, and evidence of current selection on MHC loci should be reduced in C. australis relative to C. talarum. To test these predictions, we compared variation at the MHC class II DRB and DQA genes with that at multiple neutral markers, including DQA intron 2, the mitochondrial control region, and 8–12 microsatellite loci. These analyses supported the first two of our predictions but indicated that estimates of selection (based on ω‐values) were greater for C. australis. Further exploration of these data, however, revealed differences in the time frames over which selection appears to have acted on each species, with evidence of contemporary selection on MHC loci being limited to C. talarum. Collectively, these findings indicate that demographic history can substantially influence genetic structure at functional loci and that the effects of history on selection may be temporally complex and dynamic. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 99 , 260–277.     

Genetic footprints of late Quaternary climate change in the diversity of Patagonian‐Fueguian rodents

Species are impacted by climate change at both ecological and evolutionary time scales. Studies in northern continents have provided abundant evidence of dramatic shifts in distributions of species subsequent to the last glacial maximum (LGM), particularly at high latitudes. However, little is known about the history of southern continents, especially at high latitudes. South America is the only continent, other than Antarctica, that extends beyond 40 °S. Genetic studies of a few Patagonian species have provided seemingly conflicting results, indicating either postglacial colonization from restricted glacial refugia or persistence through glacial cycles and in situ differentiation. Using mitochondrial DNA sequences of 14 species of sigmodontine rodents, a major faunal ensemble of Patagonia and Tierra del Fuego, we show that at least nine of these species bear genetic footprints of demographic expansion from single restricted sources. However, timing of demographic expansion precedes the LGM in most of these species. Four species are fragmented phylogeographically within the region. Our results indicate that (i) demographic instability in response to historical climate change has been widespread in the Patagonian‐Fueguian region, and is generally more pronounced at high latitudes in both southern and northern continents; (ii) colonization from lower latitudes is an important component of current Patagonian‐Fueguian diversity; but (iii) in situ differentiation has also contributed to species diversity.     

Breadth of Human Immunodeficiency Virus-Specific Neutralizing Activity in Sera: Clustering Analysis and Association with Clinical Variables

Induction of antibodies that neutralize a broad range of human immunodeficiency virus type 1 (HIV-1) isolates is a major goal of vaccine development. To study natural examples of broad neutralization, we analyzed sera from 103 HIV-1-infected subjects. Among progressor patients, 20% of sera neutralized more than 75% of a panel of 20 diverse viral isolates. Little activity was observed in sera from long-term nonprogressors (elite controllers). Breadth of neutralization was correlated with viral load, but not with CD4 count, history of past antiretroviral use, age, gender, race/ethnicity, or route of exposure. Clustering analysis of sera by a novel method identified a statistically robust subgrouping of sera that demonstrated broad and potent neutralization activity.Eliciting neutralizing antibodies (NAbs) against human immunodeficiency virus type 1 (HIV-1) is likely to be crucial for an optimally effective vaccine. To date, the antibodies elicited by vaccines have had weak activity against a limited spectrum of HIV-1 strains (10, 22, 33). However, many HIV-infected patients make NAbs, and a small fraction make extremely potent NAbs with broad cross-reactivity (3, 4, 9, 26, 29, 32). Understanding how a broadly reactive NAb response develops in some HIV-1-infected patients, and what viral epitopes are targeted, may provide important clues for vaccine design (18). The prevalence and clinical parameters associated with broadly reactive NAbs in serum have been the subject of much recent interest (11, 28, 29). We therefore examined the potency and breadth of neutralization in a large cohort of patients, compared breadth with clinical and demographic variables, and used clustering analysis to discern patterns in serum reactivity to diverse isolates.In a previous study (9), we screened HIV-infected patient sera for neutralizing activity against a panel of five viral isolates, using a TZM-bl Env pseudovirus neutralization assay. We also established a more robust 20-viral-isolate panel that included 10 clade B, 5 clade A, and 5 clade C Env pseudoviruses (9, 16-18). In order to evaluate the prevalence of neutralization breadth in a more quantitative manner, we studied 103 patient sera against all 20 viruses. All patients participated in National Institutes of Health clinical protocols, were infected for at least 1 year, and were antiretroviral (ARV) naïve or had been off ARVs for at least 3 months at the time of sampling. All patients were presumed to be infected with clade B virus based on locations of current and former residences. Eighty-one of the patients were included in the previously published analysis (9). Twenty-five patients were long-term nonprogressors (LTNP; also called elite controllers) from the cohort described in references 23 and 24, who typically maintain a viral load (VL) of <50 RNA copies/ml and a stable CD4⁺ T-cell count without ARV therapy; this group had a median CD4⁺ T-cell count of 850 cells/μl and a median time since HIV diagnosis of 13.5 years. The other 78 patients had a median viral load of 4,931 RNA copies/ml, a median CD4⁺ T-cell count of 534 cells/μl, and a median of 12.5 years since diagnosis. This patient group includes both typical progressors and patients without CD4⁺ T-cell decline (referred to in prior reports as slow progressors). In our previous analysis (9), we found no differences in neutralization breadth between typical and slow progressors; therefore, for the purposes of this report, both patient groups are analyzed together and collectively referred to as progressors. Dates of diagnosis but not of seroconversion were available. We calculated both the 50% and 80% inhibitory doses (ID₅₀ and ID₈₀, respectively) for each isolate using the TZM-bl assay as described in reference 31.Among progressor patients with readily detectible viremia, wide ranges of serum neutralization potency and breadth were observed (Fig. ​(Fig.1A).1A). Using a cutoff ID₅₀ of ≥100, we found that these sera neutralized a median of 10.5 (interquartile range [IQR], 5 to 14) out of 20 isolates. A total of 20% of these sera were broadly reactive, neutralizing at least 15 of 20 isolates on our panel. However, 50% of the sera neutralized 10 or fewer isolates, with several sera having very low activity despite years of untreated viremia. In contrast, sera from LTNP, with <50 copies of HIV RNA/ml plasma, had little neutralization activity, with a median of only 1 of 20 isolates neutralized with an ID₅₀ of ≥100 (IQR, 0 to 2.5) (Fig. ​(Fig.1B).1B). The range of neutralization activity in this group was similar to the lowest end of values for progressor patients. This observation concurs with previous data from our laboratory and others which show that, compared to patients with higher levels of viremia, LTNP make weak NAb responses, perhaps due to reduced antigenic stimulation of B cells (2, 9, 15, 20, 27). To ensure that we were measuring serum-mediated viral neutralization, we also incorporated ID₈₀ values into the analysis. Using a cutoff of both an ID₅₀ of ≥100 and ID₈₀ of ≥15, sera of progressor patients neutralized a median of 9 (IQR, 2.8 to 11) isolates, with 15% of sera neutralizing >75% of viruses. In contrast, among LTNP the median was 0 (IQR, 0 to 1).Open in a separate windowFIG. 1.Neutralization of 20 isolates by patient sera. (A and B) ID₅₀ value against each of 20 isolates in the TZM-bl assay is plotted for each patient. The red line indicates an ID₅₀ of 100. Input dilution was 1:10; if no neutralization was observed, the ID₅₀ is plotted as 5. (A) Progressors. (B) LTNP. (C) Viral load (RNA copies/ml plasma) versus geometric mean titer for each patient''s serum. Red circles, LTNP; black circles, progressor patients.Clinical and demographic data for all patients were compared to neutralization breadth. Two parameters were used to quantify breadth for each serum: the geometric mean ID₅₀ against the 20 isolates and the number neutralized with both an ID₅₀ of ≥100 and an ID₈₀ of ≥15. The associations between breadth and clinical covariates were tested using nonparametric methods (Spearman''s rho, Wilcoxon rank-sum, or Kruskal-Wallis test). A Bonferroni correction was used as follows to adjust for the multiple comparisons: reported P values are multiplied by 10 from the original P values and are considered significant if they are below 0.05. Viral load had a modest positive association with breadth, as measured both by geometric mean ID₅₀ (P < 0.001; r = 0.68) (Fig. ​(Fig.1C)1C) and by the number of isolates neutralized (P < 0.001; r = 0.63). When the analysis was restricted to progressor patients, this relationship still held (P = 0.008 and r = 0.37 for geometric mean ID₅₀; P = 0.050 and r = 0.31 for number neutralized). CD4⁺ T-cell count showed a modest negative correlation with breadth by both measures (P = 0.025 and r = −0.29 for geometric mean ID₅₀; P = 0.031 and r = −0.29 for number neutralized), but this relationship was not significant when LTNP were excluded from the analysis. Years since diagnosis, HLA class II alleles, risk group, history of using ARVs, race, ethnicity, gender, and age were not associated with breadth by either measure. Thus, the only strong predictor of breadth found in this cohort is viral load.To find patterns of neutralization reactivity in this data set, and determine potential common specificities of neutralization, we performed a clustering analysis based on the ID₅₀ values for the 103 sera and 20 isolates. This analysis is shown as a heat map in Fig. ​Fig.2,2, in which darker red colors indicate higher ID₅₀ values, and the data are arranged to highlight patterns of similar neutralization profiles. The data was clustered using k means, a procedure for clustering into a fixed number, k, of groups. In this procedure, the Euclidean distance between the vector of log₁₀ ID₅₀ values (i.e., the set of neutralization values in one row or column) is calculated relative to candidate group location vectors, and each serum is assigned to the cluster with the closest group location. New group means are formed on the basis of these group assignments, and this procedure is iterated until group identities do not change. This procedure was iterated 20,000 times with random initial mean vectors to find the most compact clusters. The same strategy was used to organize the isolates into serological susceptibility patterns. We added two statistical measures to assess how robust the clusters were and to determine how many clusters (k) were statistically supported in the data. To assess the impact of limited sampling, bootstrap analysis was performed by sampling the rows (or columns) with replacement 10,000 times and obtaining the fraction of times the serum (or isolate) belonged to the same cluster. The resulting degree of consensus is shown in the row or column labeled “Bootstrap” in Fig. ​Fig.2.2. To assess the impact of assay-to-assay variability, experimental “noise” was modeled from experimental data. Replicate ID₅₀ values were log₁₀ transformed, then normalized by subtracting the per-isolate or per-serum geometric means, yielding a normal distribution with a standard deviation of 0.166. Values were sampled from the distribution and added to the real data, then the k means process was repeated 1,000 times. Stability of categories for these data is shown in the row and column labeled “Noise” in Fig. ​Fig.2.2. Stability of categories for these data is shown in the row and column labeled “Noise” in Fig. ​Fig.2.2. Three clusters (k = 3) was the maximum number such that each cluster was comprised only of serum (or isolates) that were assigned to that cluster at a consistency of more than 90% by both measures of stability. Thus, each cluster shown in the boxes in Fig. ​Fig.22 represents a relatively robust grouping that would be expected to be preserved upon repeated experiments, or if different but comparable sets of sera or isolates were studied, and provides groupings of similar neutralization profiles for both sera and isolates.Open in a separate windowFIG. 2.Heat map and clustering analysis of serum. ID₅₀ values of 103 sera against 20 isolates are shown. Each row of the heat map shows ID₅₀ values for a single serum, and columns show virus isolates. Darker colors represent stronger neutralization (see key). The vertical order of sera is based on geometric mean titer; placement of clusters within this ranking uses the mean titer for all cluster members. The bars labeled “Bootstrap” or “Noise” show the results of statistical analysis of clustering. Both are visualized by mixing red, yellow, and blue corresponding to the relative frequencies of matched group assignments. A bright red, yellow, or blue color is a categorization that is unambiguous. Sera or isolates are grouped if they have a categorization of 90% or greater consistency by both the bootstrap and noise tests. Boxes highlight the clusters. Sera in red type are from LTNP. Clusters of patient sera are labeled P1, P2, and P3, while clusters of isolates are labeled I1, I2, and I3.Serum cluster P1 included the majority of LTNP sera and a few additional low potency/breadth sera (Fig. ​(Fig.2).2). Serum cluster P3 included 24 sera with the greatest potency and breadth of neutralization. In addition to showing sera and isolate k means clusters, we ordered the columns and rows in the heat map according to their geometric mean values to better visualize like patterns in the columns and rows. Five sera have higher geometric means than serum cluster 3 so are placed at the bottom of Fig. ​Fig.2.2. Among the sera with intermediate activity, one robust cluster was defined as follows: sera in this cluster (P2) do not neutralize the isolates most difficult to neutralize (geometric mean ID₅₀, 18 for P2 sera versus I3 isolates) but do neutralize isolates in the other two isolate clusters (geometric mean titers 297 and 110 for P2 sera versus I3 and I2, respectively). Thus, the patient clusters are defined not only by overall breadth or potency, but also by which isolates are neutralized. Of the clinical variables measured, only viral load was significantly associated with membership in a cluster: median VL is lower in patients with sera in cluster P1, which contains most of the LTNP, than in those with non-P1 sera (Bonferroni corrected P < 0.001).Figure ​Figure22 also shows that the panel of 20 diverse viral isolates we used to study breadth could be categorized into three clusters. Isolate cluster 3 (I3) consists of two B clade Envs, JRFL and BaL.01, which are the most neutralization-sensitive in the panel. Each of the other two clusters contains isolates of different clades. Four of five clade C isolates are in the most resistant cluster I1; these isolates are known to be sensitive to clade C sera but more resistant to clade B sera (11, 17). Of note, genetically closely related viruses were not always found within the same neutralization-susceptibility cluster. For example, isolates Q769.d22 and Q769.h5, both clade A, contain two different envelope proteins from the same patient but do not appear in the same cluster. These isolates are known to have differing sensitivities to autologous plasma and to MAb (6).We also analyzed neutralization titers of soluble CD4 (sCD4) and the commonly used monoclonal antibodies (MAb) 2G12, 4E10, 2F5, b12, and 447-52d against the same 20 isolates. The epitopes targeted by these MAb are well defined (7). It was possible that the isolate clustering of sera shown in Fig. ​Fig.22 was driven by responses that are directed mainly to epitopes thought to confer neutralization breadth. If this were the case, we hypothesized that commonalities between neutralization mediated by MAb and by sera might be observed. Clustering analysis of MAbs (Fig. ​(Fig.3)3) was performed as described above. The clustering based on patterns of neutralization by MAbs was less statistically robust than that calculated with serum titers, with no clusters found at 90% bootstrap confidence. Figure ​Figure33 shows the three clusters of isolates that appeared in only 75% of bootstrap and noise replicates. These isolate clusters (with the exception of the sensitive JRFL/Bal.01 cluster I3) were completely interspersed with those defined in the serum analysis. To further examine the relationship of serum and MAb reactivity, we compared membership of an Env in an isolate cluster as defined by serum titers (Fig. ​(Fig.2)2) with its sensitivity to each individual MAb. While membership in isolate cluster I3 correlated with neutralization titers of two of the MAb, b12 and 447-52d (P < 0.01 for each for both JRFL and BaL.01), membership in clusters I1 and I2 as defined by serum titers did not correlate with sensitivity to any one MAb. These data suggest that the clustering based on serum titers was not defined by any single epitope matching the MAb specificities.Open in a separate windowFIG. 3.Heat map and clustering analysis of monoclonal antibodies and sCD4. Each row of the heat map shows IC₅₀ values for a single reagent, and columns show virus isolates. See legend to Fig. ​Fig.22 for an explanation. Boxes show isolates assigned to clusters at the 75% level. Neutralization data are from references 16 and 17 and this study.This clustering analysis allowed us to discern patterns of neutralization reactivity that are distinct from clade and from sensitivity to known cross-neutralizing MAbs. The appearance of isolates from multiple clades in clusters I1 and I2 is consistent with prior analyses of MAbs (5) and sera (11, 14) in which sequences from the same clade were distributed among multiple clusters. In general, the clade of a virus does not predict its sensitivity to patient sera and is not directly equivalent to a neutralization serotype (13, 21, 26, 34). Furthermore, the discordance between the results for MAbs and for sera may suggest that the clustering based on serum titers was not dominated by reactivities that are similar to those of the MAbs. It is unclear at present whether these differences are mediated by targeting of conserved epitopes that are not yet identified, epitopes similar to those of MAbs but with differences in neutralization patterns, or multiple specificities. These findings are potentially consistent with antibody cloning (30) and serum mapping (4, 8, 11, 18, 19, 29) studies which found that in some sera, multiple specificities are responsible for the breadth of neutralization. Future studies of this cohort using additional isolates may allow determination of possible neutralization serotypes, and viral sequence motifs that are signatures of neutralization cluster, as for the clade C sera analyzed in reference 14, or neutralization sensitivity.The clinical data suggest that extended exposure to antigen may be beneficial for the development of broad NAbs. We observed that viral load has a modest positive correlation with neutralization breadth (Fig. ​(Fig.1C),1C), as was also seen in cohorts in the United States (29) and Kenya (28). Conversely, LTNP with a VL of <50 rarely had breadth (Fig. ​(Fig.1B),1B), again consistent with other reports (1, 27). Length of exposure also seems to play a role in the development of broad NAbs: Sather et al. (29) noted an association of breadth with the duration of infection in a seroconversion cohort. These associations are modest, and several slow progressors had low NAbs; thus, antigen may be necessary but not sufficient for the development of broad NAbs. Long-term exposure to HIV antigen has been shown to directly impact immunoglobulin G (IgG) development: Scheid et al. (30) found that Env-specific IgG genes were highly mutated compared to other IgG genes in patients with broad NAbs, implying multiple rounds of selection and hypermutation in response to persistence or turnover of viral antigen. Collectively, these data suggest that a vaccine may need to supply viral antigen for long periods of time, via multiple dosing or a replication-competent vector, to allow antibody maturation and development of a broad neutralizing response.The prevalence and titers of NAbs in chronically HIV-infected patients provide encouragement for the development of vaccines that elicit protective humoral immunity. We found that that 20% of progressor patients make broad NAbs; although our cohort is enriched for slow progressors, similar frequencies were noted in other, less-selected cohorts (11, 29, 32). The fact that so many patients make broad NAbs, even in the setting of B-cell dysfunction caused by HIV (25), demonstrates the ability of the human immune system to generate such NAbs. An appropriate vaccine given to immunocompetent individuals could potentially elicit broad NAbs at higher frequencies. Furthermore, most patients neutralized at least some isolates with titers in the hundreds in the TZM-bl assay. Data from a recent passive transfer experiment in a low-dose-challenge simian-HIV (SHIV) macaque model demonstrated a protective effect of neutralizing titers of 1:200 in the TZM-bl assay (12). Thus, these examples show that it is possible to elicit NAbs at sufficient levels and breadth to potentially contribute to the protective efficacy of an HIV vaccine.     

Cell organisation,sulphur metabolism and ion transport-related genes are differentially expressed in Paracoccidioides brasiliensis mycelium and yeast cells

Background

Bread wheat (Triticum aestivum) is an important staple food. However, wheat gluten proteins cause celiac disease (CD) in 0.5 to 1% of the general population. Among these proteins, the α-gliadins contain several peptides that are associated to the disease.

Results

We obtained 230 distinct α-gliadin gene sequences from severaldiploid wheat species representing the ancestral A, B, and D genomes of the hexaploid bread wheat. The large majority of these sequences (87%) contained an internal stop codon. All α-gliadin sequences could be distinguished according to the genome of origin on the basis of sequence similarity, of the average length of the polyglutamine repeats, and of the differences in the presence of four peptides that have been identified as T cell stimulatory epitopes in CD patients through binding to HLA-DQ2/8. By sequence similarity, α-gliadins from the public database of hexaploid T. aestivum could be assigned directly to chromosome 6A, 6B, or 6D. T. monococcum (A genome) sequences, as well as those from chromosome 6A of bread wheat, almost invariably contained epitope glia-α9 and glia-α20, but never the intact epitopes glia-α and glia-α2. A number of sequences from T. speltoides, as well as a number of sequences fromchromosome 6B of bread wheat, did not contain any of the four T cell epitopes screened for. The sequences from T. tauschii (D genome), as well as those from chromosome 6D of bread wheat, were found to contain all of these T cell epitopes in variable combinations per gene. The differences in epitope composition resulted mainly from point mutations. These substitutions appeared to be genome specific.

Conclusion

Our analysis shows that α-gliadin sequences from the three genomes of bread wheat form distinct groups. The four known T cell stimulatory epitopes are distributed non-randomly across the sequences, indicating that the three genomes contribute differently to epitope content. A systematic analysis of all known epitopes in gliadins and glutenins will lead to better understanding of the differences in toxiCity among wheat varieties. On the basis of such insight, breeding strategies can be designed to generate less toxic varieties of wheat which may be tolerated by at least part of the CD patient population.     

Light-to-dark transitions trigger a transient increase in intracellular Ca2+ modulated by the redox state of the photosynthetic electron transport chain in the cyanobacterium Anabaena sp. PCC7120

Light‐to‐dark transitions represent one of the most crucial environmental stresses that photosynthetic organisms must cope with, since substantial metabolism adaptations are required in order to utilize alternative energy and carbon sources. Although signal transduction systems for changing light regimes are not sufficiently understood, calcium has been implicated in plants as a second messenger in light‐on and light‐off events. Much less is known about light signalling in cyanobacteria, but it has been shown that calcium probably performs similar signalling roles in these organisms and other prokaryotes. Herein it is reported that light‐to‐dark transitions trigger a calcium transient in aequorin expressing Anabaena sp. PCC7120. The magnitude of this transient depends on the fluence rate previously irradiated and can reach a peak height over 2 µm free calcium when the fluence rate of light is around 400 µmol photons s^?1 m^?2. The use of increasing calcium concentration, ethylene glycol‐bis (β‐aminoethylether) N,N,N′,N′‐tetraacetic acid (EGTA), verapamil and trifluoperazine indicated that these transients are originated by a calcium influx probably through verapamil‐sensitive Ca²⁺ channels and are probably modulated by calcium‐binding proteins. Experiments with different light spectral qualities and the photosynthetic inhibitors 3‐(3,4 dichlorophenyl)1,1,dimelthylurea (DCMU) and 3,5‐dibromo‐3‐methyl‐b‐isopropyl‐p‐benzoquinone (DBMIB) indicate that the calcium transient triggered by the light‐to‐dark transition is not coupled to a specific photoreceptor but rather to changes in the redox state of photosynthetic electron transport chain components other than the plastoquinone pool.